Sales

Product Q&A Expert

Answer product questions using curated knowledge and web research

Overview Capabilites Agent Workflow Example prompt

Overview

The Product Q&A Expert answers detailed product questions by combining your curated product knowledge base with real-time web research, providing accurate, comprehensive responses that help sales and support teams close deals and resolve customer inquiries. Sales reps and support agents face complex product questions that require synthesizing information from multiple sources—product docs, technical specifications, competitive intelligence, and industry context. This agent searches your knowledge base, supplements with web research when needed, and delivers complete, accurate answers with source citations. It accelerates response times, ensures consistency, and empowers teams with instant access to product expertise.

Capabilities

Answer product questions using internal knowledge base and documentation
Supplement with web research for competitive and industry context
Provide source citations for all information and claims
Tailor answer complexity to questioner's technical level
Flag questions requiring human expert review or clarification

Agent Workflow

Input: User submits product question or customer inquiry
Knowledge Search: Agent searches internal product documentation and knowledge base
Research Supplement: Conducts web research if additional context needed
Answer Synthesis: Combines sources into comprehensive, accurate response
Citation: Includes source references for transparency and verification
Output: Delivers complete answer with sources and confidence level

Example prompt

"A prospect is asking: 'Does your analytics platform support real-time data streaming from Kafka, and how does your query performance compare to Snowflake for datasets over 100TB?' Search our product documentation to answer the Kafka integration question with specific details about supported versions and configuration. Then research Snowflake's performance benchmarks for large datasets and provide an honest comparison of our query performance, citing specific metrics from our docs and any relevant third-party benchmarks. If we don't have clear performance data for 100TB+ datasets, flag that we need to involve our solutions engineering team."